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Learning the structure of linear latent variable models
 Journal of Machine Learning Research
, 2006
"... We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are dseparated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the ..."
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Cited by 41 (13 self)
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We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are dseparated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is pointwise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we
Generalized measurement models
, 2004
"... Given a set of random variables, it is often the case that their associations can be explained by hidden common causes. We present a set of welldefined assumptions and a provably correct algorithm that allow us to identify some of such hidden common causes. The assumptions are fairly general and so ..."
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Cited by 7 (4 self)
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Given a set of random variables, it is often the case that their associations can be explained by hidden common causes. We present a set of welldefined assumptions and a provably correct algorithm that allow us to identify some of such hidden common causes. The assumptions are fairly general and sometimes weaker than those used in practice by, for instance, econometricians, psychometricians, social scientists and in many other fields where latent variable models are important and tools such as factor analysis are applicable. The goal is automated knowledge discovery: identifying latent variables that can be used across diferent applications and causal models and throw new insights over a data generating process. Our approach is evaluated throught simulations and three realworld cases.
Discovery of latent structures: Experience with the CoIL challenge 2000 data set
 Journal of Systems Science and Complexity
, 2008
"... Abstract The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier by Zhang (2002), but that study involved only small applications with 4 or 5 observed vari ..."
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Cited by 5 (3 self)
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Abstract The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class (HLC) models. A similar effort was made earlier by Zhang (2002), but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data. Key words Bayesian networks, case study, latent structure discovery, learning. 1
Automatic discovery of latent variable models
 Machine Learning Dpt., CMU
, 2005
"... representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. ..."
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Cited by 5 (4 self)
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representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity.
New dseparation identification results for learning continuous latent variable models
 Proceedings of the 22nd Interational Conference in Machine Learning
, 2005
"... Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the dseparations that defi ..."
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Cited by 2 (2 self)
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Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hidden variables cannot be directly observed, one has to rely on alternative methods to identify the dseparations that define the graphical structure. This paper describes new distributionfree techniques for identifying dseparations in continuous latent variable models when nonlinear dependencies are allowed among hidden variables. 1.
QuartetBased Learning of Hierarchical Latent Class Models:
"... Hierarchical latent class (HLC) models are treestructured Bayesian networks where leaf nodes are observed while internal nodes are hidden. The currently most e#cient algorithm for learning HLC models can deal with only a few dozen observed variables. ..."
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Hierarchical latent class (HLC) models are treestructured Bayesian networks where leaf nodes are observed while internal nodes are hidden. The currently most e#cient algorithm for learning HLC models can deal with only a few dozen observed variables.
QuartetBased Learning of Shallow Latent Variables
"... Hierarchical latent class(HLC) models are treestructured Bayesian networks where leaf nodes are observed while internal nodes are hidden. We explore the following twostage approach for learning HLC models: One first identifies the shallow latent variables – latent variables adjacent to observed va ..."
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Hierarchical latent class(HLC) models are treestructured Bayesian networks where leaf nodes are observed while internal nodes are hidden. We explore the following twostage approach for learning HLC models: One first identifies the shallow latent variables – latent variables adjacent to observed variables – and then determines the structure among the shallow and possibly some other “deep ” latent variables. This paper is concerned with the first stage. In earlier work, we have shown how shallow latent variables can be correctly identified from quartet submodels if one could learn them without errors. In reality, one does make errors when learning quartet submodels. In this paper, we study the probability of such errors and propose a method that can reliably identify shallow latent variables despite of the errors. 1
Learning Associations by Discrete Measurement Models ABSTRACT
"... Discovering interesting associations in discrete databases is a key task in data mining. Association rules and graphical models among observed variables are standard tools in this analysis, but in problems where associations are due to hidden common causes not recorded in the database, the resulting ..."
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Discovering interesting associations in discrete databases is a key task in data mining. Association rules and graphical models among observed variables are standard tools in this analysis, but in problems where associations are due to hidden common causes not recorded in the database, the resulting models are overly complex and offer no picture of the causes of such dependencies. For instance, the pattern of answers in a large marketing survey might be explained by a few latent traits of the population. A large set of association rules might offer little insight on this process. Instead, one can model the observed variables as measurements of latent concepts, such as in discrete principal component analysis. However, discrete PCA and its variations rely on the assumption that latents are independent. While such an assumption might be reasonable in, e.g., black box models for classification, it makes little sense if the goal is understanding the real causes for the associations. We present in this paper a method for finding hidden common causes that explain observed associations of subsets of the given variables without imposing independence constraints over latents. Variables should be binary or ordinal.